Implementation of Machine Learning Based Interpreter for Real Time Sign Language Detection and Action Recognition

In today’s increasingly interconnected world, communication stands as a fundamental human right, yet many face significant challenges in expressing themselves due to various communication barriers. Individuals who are deaf or hard of hearing encounter substantial hurdles in daily interactions, highlighting the need for innovative solutions to bridge this gap. Sign language emerges as a powerful means of communication, offering a rich and expressive language utilized by millions worldwide. The proposed research represents a revolutionary way designed to empower and enhance the lives of individuals who are deaf or hard of hearing by leveraging cutting-edge machine learning technologies. The primary objective of this project is to develop a state-of-the-art Sign Language Converter, facilitating seamless communication between sign language users and those who are not fluent in the language. By creating a tool that bridges the gap between hearing and non-hearing communities. The proposed research involves the development of a sign language analyzer powered by machine learning. The analyzer analyses signs performed by users and matches them to corresponding letters or words in a predefined dataset. This user-friendly interface will be designed to promote real-time sign language conversion, ensuring seamless communication between users. Developing a robust machine learning model for sign language recognition, ensures accessibility for individuals with varying degrees of hearing impairments and technological proficiency, and planning for scalability and long-term sustainability. The work can revolutionize communication for individuals with hearing impairments by breaking down barriers and enabling effective interaction in diverse social contexts.